Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang, Qingling; Li, Bin; Thau, David; Moore, Rebecca

doi:10.3390/rs70911887

Cited by 62 publications

(46 citation statements)

References 60 publications

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“…Another advantage of using NISI is its capability in further improving the spatial structure in urban landscapes, thus NISI has better ISA prediction performance than HSI and VANUI when ISA is high. As global Landsat data are available, the integration of DMSP-OLS and Landsat NDVI will be another choice for improving ISA mapping [55]. The advance of computing (e.g., Google Earth Engine, Google Cloud) and satellite technologies will make the effective use of multi-source data an important research topic in improving ISA mapping performance at regional and global scales [18].…”

Section: Discussionmentioning

confidence: 99%

Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Guo

Kuang

2017

Remote Sensing

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Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) data have been used for ISA mapping, but high uncertainty existed due to mixed-pixel and data-saturation problems. This paper presents a new index called normalized impervious surface index (NISI), which is an integration of DMSP-OLS and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, in order to reduce these problems. Meanwhile, this newly developed index is compared with previously used indices-Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI)-in ISA mapping performance. We selected China as an example to map fractional ISA distribution through a support vector regression approach based on the relationship between the index and Landsat-derived ISA data. The results indicate that the proposed NISI provided better ISA estimation accuracy than HSI and VANUI, especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e., <0.2). This approach can be used to rapidly update ISA datasets at regional and global scales.

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Section: Discussionmentioning

confidence: 99%

Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Guo

Kuang

2017

Remote Sensing

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“…The vegetation indices are generated by plotting the vegetation index value of the pixel containing the plot for each date that imagery is available (Figure 4). In the default configuration of Collect Earth, persistent clouds and cloud shadows will interfere with the index values gathered from ground-based features in the landscape [43]. The imagery and analytical processes used to generate the vegetation index charts can be modified in GEE Code Editor; however, the historic time range possible will be limited by the imagery acquisition dates of the archives referenced (for example, Landsat 7 was launched in 1999, and its data became available globally from 2001 onward).…”

Section: Data Collection Framework For Augmented Visual Interpretationmentioning

confidence: 99%

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

et al. 2016

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Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth's structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world's land use and land cover.

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“…In 2015, Zhang, Q.L. proposed a new NDUI index, which is a combination of the NDVI and NTL indexes to quickly depict city structures [55]. (Figure 7).…”

Section: Application-oriented Communitiesmentioning

confidence: 99%

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

Guan

et al. 2017

Remote Sensing

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Abstract:In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and Social Network Analysis (SNA), we drew several conclusions: (1) NTL related studies have become a research hotspot, especially after 2011 when the second generation of NTL satellites, the Suomi National Polar-orbiting Partnership (S-NPP) Satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was on board. In the same period, the open-access policy of the long historical dataset of the first generation satellite Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) started. (2) Most related studies are conducted by authors from USA and China, and the USA takes the lead in the field. We identified the biggest research communities constructed by co-authorships and the related important authors and topics by SNA. (3) By the visualization and analysis of the topic evolution using the co-word and co-cited reference networks, we can clearly see that: the research topics change from hardware oriented studies to more real-world applications; and from the first generation of the satellite DMSP/OLS to the second generation of satellite S-NPP. Although the Day Night Band (DNB) of the S-NPP exhibits higher spatial and radiometric resolution and better calibration conditions than the first generation DMSP/OLS, the longer historical datasets in DMSP/OLS are still important in long-term and large-scale human activity analysis. (4) In line with the intuitive knowledge, the NTL remote sensing related studies display stronger connections (such as interpretive frame, context, and academic purpose) to the social sciences than the general remote sensing discipline. The citation trajectories are visualized based on the dual-maps, thus the research preferences for combining the environmental, ecological, economic, and political science disciplines are clearly exhibited. Overall, the picture of the NTL remote sensing research is presented from the scientist-level, topic-level, and discipline-level interactions. Based on these analyses, we also discuss the possible trends in the future work, such as combining NTL studies with social science research and social media data.

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Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Cited by 62 publications

References 60 publications

Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

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